Multi-objective optimization of reactive power dispatch problem using fuzzy tuned mayfly algorithm

The recent multi-objective optimization problems are very complex which require an effective and robust evolutionary method for obtaining a global Pareto-optimal solutions. Most of these multi-objective evolutionary techniques are population-based methods which usually work on random search approach...

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Vydané v:Expert systems with applications Ročník 249; s. 123819
Hlavní autori: Gangil, Gaurav, Kumar Goyal, Sunil, Saraswat, Amit
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.09.2024
Predmet:
Adaptive evolutionary method
Fuzzy controller
Fuzzy Tuned Mayfly Algorithm
Multi-objective optimization
Pareto-optimal solutions
Fuzzy Tuned Mayfly Algorithm
Multi-objective optimization
Fuzzy controller
Pareto-optimal solutions
Adaptive evolutionary method
ISSN:0957-4174, 1873-6793
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Shrnutí:The recent multi-objective optimization problems are very complex which require an effective and robust evolutionary method for obtaining a global Pareto-optimal solutions. Most of these multi-objective evolutionary techniques are population-based methods which usually work on random search approaches and often trapped into local minima during their execution. A new fuzzy tuned mayfly algorithm (FTMA) is presented in this paper, to obtain the Pareto-optimal solutions for any complex multi-objective power system problem. It has a self-adapting global exploration capability to get the best Pareto-optimal solutions by varying two crucial parameters i.e., crossover (Pc) and mutation Pm probabilities. Moreover, a model of Pareto-dominance is employed to rank non-dominating solutions to maintain the diversity within the populations. The proposed evolutionary approach is examined on a standard ZDT benchmark test suite and its algorithmic performance is statistically compared with a few of the recent optimization techniques such as NSGA-II, NSGA-III, MMODE, MOAVOA and MMA algorithm. It is further applied to minimize two distinct objective functions (i.e., total transmission loss and voltage stability index) for IEEE-118 bus system and IEEE-24 bus RTS. A comprehensive statistical analysis is presented for the obtained numerical results, which proves that proposed FTMA has better convergence with better solution diversity in comparison to the other competing algorithms. A comprehensive analysis based on statistical results reveal that the proposed FTMA is superior for obtaining better non-dominating pareto-fronts as compared to other competing methods.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2024.123819